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Abstract

We consider the problem of discovering discriminative
exemplars suitable for object detection. Due to the diver-
sity in appearance in real world objects, an object detec-
tor must capture variations in scale, viewpoint, illumination
etc. The current approaches do this by using mixtures of
models, where each mixture is designed to capture one (or
a few) axis of variation. Current methods usually rely on
heuristics to capture these variations; however, it is unclear
which axes of variation exist and are relevant to a particular
task. Another issue is the requirement of a large set of train-
ing images to capture such variations. Current methods
do not scale to large training sets either because of train-
ing time complexity [31] or test time complexity [26]. In
this work, we explore the idea of compactly capturing task-
appropriate variation from the data itself. We propose a two
stage data-driven process, which selects and learns a com-
pact set of exemplar models for object detection. These se-
lected models have an inherent ranking, which can be used
for anytime/budgeted detection scenarios. Another benefit
of our approach (beyond the computational speedup) is that
the selected set of exemplar models performs better than the
entire set.